1. Field
The present disclosure relates to systems, methods, and devices for controlling neural prosthetic devices and electrophysiological recording equipment, and for using the same in clinical operation.
2. Related Art
All publications herein are incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference. The following description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
The ability to interact directly with the nervous system to control a computer cursor or robot arm has been demonstrated by several researchers. See, e.g., J. Wessberg et al., “Real-time prediction of hand trajectory by ensembles of cortical neurons in primates,” Nature, 408(6810): 361-365 (2000); J. M. Carmena et al., “Learning to control a brain-machine interface for reaching and -grasping by primates,” PLoS, 1:193-208 (2003); D. M. Taylor et al., “Direct cortical control of 3D neuroprosthetic devices,” Science, 296:1829-1832 (2002); P. R. Kennedy and R. A. Bakay, “Restoration of neural output from a paralyzed patient by a direct brain connection,” Neuroreport, 9(8):1707-11 (1998); and R. A. Andersen et al., “Cognitive Neural Prosthetics,” Trends in Cog. Sci., 8(11):486-493 (November 2004).
These advances in neural prosthetic systems may provide patients with lost motor function due to spinal cord injury, stroke, neurodegenerative diseases, and the like with the ability to regain access to their surroundings. Despite these breakthroughs, however, many challenges remain. See, e.g., J. P. Donoghue, “Connecting cortex to machines: recent advances in brain interfaces,” Nature Neurosci, 5:1085-1088A (2002). A fundamental problem, for instance, lies in creating interface devices capable of sustaining interaction with neuronal populations for long periods of time in a practical and reliable manner. Long-term neural interfacing demands that the overall device be implantable, safe, and minimally obtrusive. Further, the device should require minimal maintenance.
Information transfer and processing in the brain occurs through the transmission of electrical pulses, called action potentials, between neurons. Information about the various areas or regions of the brain may be gained by studying patterns of action potentials associated with individual neurons while a subject (e.g., a rat, fly, monkey, or human) is presented with a stimulus or engages in a behavioral task. While noninvasive methods such as fMRI or EEG recordings can provide gross estimates of activity levels in a particular region of the brain, action potentials of individual neurons must be examined to understand how information is processed within local neural networks.
Action potentials may be recorded extracellularly by inserting electrodes (typically sharpened metal wires insulated along their length and exposed at the tip) into the neural tissue. Because action potentials emitted by a neuron are highly stereotyped in shape and information is encoded in their timing, a successful extracellular recording is one in which the firing of action potentials of individual neurons can reliably be detected. These neurons are then considered “isolated” in the recording. Isolated neural recordings may be essential to the proper function of a neural prosthetic. Such recordings may also form the basis for fundamental scientific investigations into the function of the brain and the means by which information is encoded in neural networks.
There are two dominant modes of recording: acute and chronic. In acute recording, electrodes are inserted and removed from the neural tissue during each recording session. In chronic recording, electrodes are surgically implanted and remain in place for weeks, months, or possibly years at a time. As used herein, the term semi-chronic recording is used to refer to a recording made by electrodes implanted in neural tissue for a period of time longer than a single recording session but somewhat shorter than the duration of implantation used with chronic recordings. For example, the implants used to make a semi-chronic recording may be implanted several days or weeks.
For acute recordings, a portion of the skull over the brain region of interest is removed and replaced with a sealable chamber. During a recording session, a device termed a microdrive is affixed to the opened chamber and used to advance the electrodes into the neural tissue, usually in a motorized fashion. The electrodes are advanced along a straight line, with the axis of penetration chosen by an experimenter or operator. In conventional practice, the electrode motion is controlled manually by the operator until one or more neurons is/are sufficiently isolated. This process is commonly guided by experience, intuition, and feedback from visual and auditory representations of the voltage signal detected by the electrodes. Such acute recordings are typically used for basic scientific research, but they may also be used to implement a neural prosthetic in a semi-chronic fashion.
Typically, the goal is to position each electrode of the microdrive close enough to a single and unique neuron for a high quality recording of the electrical activity of the neuron, yet far enough away to avoid damaging the neuron. In this manner, the number of neurons recorded may correspond to the number of electrodes. Normally, the electrical recording site must be within a 40 micron to 60 micron radius and preferably about a 50 micron radius of the unique neuron's soma to obtain an extracellular signal that can be successfully differentiated from background noise. See, e.g., C. Gray et al., “Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex,” J. Neurosci. Methods, 63:43-54 (1995). Without such successful positioning, an electrode immersed in neural tissue may not successfully record any neural signals, thereby rendering the electrode useless. During the course of a typical recording session, such as may occur during a basic scientific experiment or the simulation of a neural prosthetic, each of the electrodes must be repositioned periodically to maintain a desired level of signal quality. Repositioning may be necessitated by tissue migration and/or decompression that occurs naturally. The process of isolating and maintaining neural signals consumes a significant amount of the operator's time and focus. The considerable time and effort needed to affect the neural isolations considerably reduces the efficiency with which electrophysiological recording experiments can be performed.
Simultaneous recordings made with many electrodes are becoming an increasingly important technique for understanding how local networks of neurons process information, as well as how brain areas communicate with each other. Commercial microdrives (i.e., motorized electrodes that receive movement commands provided manually by a human operator) with sixteen or more electrodes are currently available. See, e.g., S. Baker et al., “Multiple single unit recording in the cortex of monkeys using independently moveable microelectrodes,” J. Neurosci. Methods, 94(1):5-17 (1999). As the number of electrodes increases, the task of positioning each electrode to maintain a high quality neural signal becomes intractable for a single operator to manage. Data collection in experiments that use multiple electrodes is limited by how many neural signal channels the operator can effectively monitor.
In chronic recordings (which are the most conventional type of recordings used as the front end of a neural prosthesis), stationary multi-electrode assemblies, which are typically bundles or arrays of thin wires or silicon probes, are surgically implanted in the region of interest. See, e.g., I. Porada et al., “Rabbit and monkey visual cortex: more than a year of recording with up to 64 microelectrodes,” J. Neurosci. Methods, 95:13-28 (2000); J. Williams et al., “Long-term neural recording characteristics of wire microelectrode arrays implanted in cerebral cortex,” Brain Res. Protocols, 4:303-13(1999); and P. Rousche and R. Normann, “Chronic recording capability of the Utah intracortical electrode array in cat sensory cortex,” J. Neurosci. Methods, 82:1-15 (1998).
The signal yield of the implanted array (i.e. the percentage of the electrodes of the array that record active neurons) depends upon the luck of the initial surgical placement. As mentioned above, it is believed that the electrically active tip of a recording electrode must lie within approximately 40-60 microns of the neuron's soma to provide a useful signal. The neurons close enough to a particular electrode may not encode the proper task for the prosthetic system, rendering that electrode practically useless. Unfortunately, in some cases, one or more of the electrodes may be placed in inactive tissue or the wrong brain region. Even if properly placed, the active recording site of the electrode may not sit sufficiently close to an active neuron. Moreover, even if the electrode is initially well placed, tissue migrations (e.g., caused by blood pressure variations, breathing, and mechanical shocks), inflammation, neuron expiration, reactive gliosis, and other local tissue reactions can cause subsequent loss of signal; thereby reducing or disabling the function of the recording array over time.
To date, all practical neuroprosthetic systems have used implanted multi-electrode arrays whose electrodes have a fixed geometry. These fixed geometries suffer from the problems outlined above.
A chronic implant in which the electrodes can be continually repositioned after implantation may overcome these limitations and greatly extend the signal yield and lifetime of chronic array implants. Longevity of chronically implanted electrode arrays is necessary because repeated and frequent surgical intervention to implant new electrodes is not desirable, and may place the subject (e.g., a neuroprosthetic patient) at greater risk for surgical complications.
Alternatively, one could use a miniature chronic microdrive of the type often used in basic neuroscience research. These simple microdrives are typically implanted in non-human primates, rats, mice, and rabbits to enable chronic recordings. In such devices, each of the electrodes may be repositioned manually by either turning lead screws or temporarily connecting a conventional motorized microdrive (of the type typically used in acute recordings) to the array in order to adjust the position of each of the electrodes. See, e.g., P. D. Wall, J. Freeman, D. Major, “Dorsal horn cells in spinal and in freely moving rats,” Exp Neural, 19: 519-529 (1967); J. L. Kubie, “A Driveable bundle of microwires for collecting single-unit data from freely-moving rats,” Physiology & Behavior, 32: 115-118 (1984); B. P. Vos et al., “Miniature carrier with six independently moveable electrodes for recording of multiple single-units in the cerebellar cortex of awake rats,” J Neurosci Methods, 94: 19-26 (1999); S. Venkatachalam et al., “Ultra-miniature headstage with 6-channel drive and vacuum-assisted micro-wire implantation for chronic recording from the neocortex,” J Neurosci Methods, 90: 37-46 (1999); J. D. Kralik et al., “Techniques for long-term multisite neuronal ensemble recordings in behaving animals,” J. Neurosci. Meth., 25:121-50 (2001); A. S. Tolias et al., “Coding visual information at the level of populations of neurons,” Program No. 557.5., 2002 Abstract Viewer/Itinerary Planner, Washington, D.C.: Society for Neuroscience (2002); J. G. Keating and G. L. Gerstein, “A chronic multi-electrode microdrive for small animals,” J Neurosci Meth., 117: 201-206 (2002); K. L. Hoffman and B. L. McNaughton, “Coordinated reactivation of distributed memory traces in primate neocortex,” Science, 297: 2070-2073 (2002); and R. C. deCharms, et al., “A multielectrode implant device for the cerebral cortex,” J Neurosci Meth., 93: 27-35 (1999).
Even if motorized, chronic microdrives typically face the challenge of requiring constant human supervision to reposition the electrodes to achieve a desired level of signal quality. This process can become tedious and even impractical (particularly if the array is used as part of neural prosthetic) as the number of electrodes increases. See S. N. Baker et al., “Multiple single unit recording in the cortex of monkeys using independently moveable microelectrodes,” J. Neurosci. Meth., 94:5-17 (1999); and M. S. Fee and A. Leonardo “Miniature motorized microdrive and commutator system for chronic neural recording in small animals,” J Neurosci Methods, 112: 83-94 (2001).
The inventors have earlier described initial steps towards a chronic multi-electrode implant in which the electrodes can be continually and autonomously repositioned after implantation. See E. Branchaud et al., “A Miniature Robot for Autonomous Single Neuron Recordings,” IEEE Conf. on Robotics and Automation, Barcelona, Spain (April 2005); Cham et al., “Semi-chronic motorized microdrive and control algorithm for autonomously isolating and maintaining optimal extracellular action potentials,” J. Neurophysiol., 93(1):570-579 (January 2005); and C. Pang et al., “A New Multi-Site Probe Array with Monolithically Integrated Parylene Flexible Cable for a Neural Prosthesis,” Proc. 27th Conf. IEEE-EMBS (2005). To be useful in a clinical application, such as using neural recordings to generate control signals for an external device (e.g., neural prosthetics), the position of the electrodes of the chronic multi-electrode implant must be autonomously controlled to maintain a desirable level of signal quality. Additionally, an autonomous control algorithm that could position electrodes in a chronic multi-electrode implant could also be useful for the control of electrode positioning during an acute recording experiment of the type used in many neuroscience research laboratories.
In order for the chronic multi-electrode implant to operate autonomously and without the aid of an operator, the neuron isolation and signal quality maintenance functions performed by the operator must be automated. When isolating a neuron, the operator performs a number of difficult tasks, including event detection (i.e., detecting the presence and onset of an action potential), unsupervised classification of neural signals (i.e., classifying neural events without a priori knowledge of their number and structure), and accounting for stochastic neuron activity and complex mechanical interactions between the electrode and the neural tissue. Following is a brief discussion of several of the major challenges faced in automating the isolation process.
Unsupervised detection, classification, and data association can present a challenge. Action potentials of varying amplitudes and shapes must be autonomously detected and grouped by the neuron from which they originated. In conventional practices, this process is normally performed by the operator who manually sets thresholds and identifies distinguishing signal features. The data association problem is also faced when attempting to track the signals arising from distinct neurons while moving the electrode.
Variable firing rates can also present a challenge. A general procedure for autonomously isolating a neuron involves sampling the amplitude of action potentials at several locations and searching for the local maximum of the signal quality. Depending on the behavioral state of the recording subject, the neuron that is being isolated may stop firing action potentials for one or more sampling periods, leading to false estimates of the signal amplitude at those locations.
During the initial insertion of the electrodes, neural tissue is compressed, and subsequent decompression causes the neurons to drift relative to the electrode. Optimal recording positions are moving targets. It is quite common for action potentials that have been observed for some time to disappear; presumably, the neuron has either drifted out of range or stopped firing. Also, after a neuron has been isolated, the electrode must be readjusted periodically to maintain the isolation. Often, neurons drift away from the line of travel of the electrode and become impossible to isolate or reisolate.
Local electrode-tissue interactions can be a challenge. In addition to the gross tissue relaxation occurring over several hours of an experiment, local mechanical coupling between the electrode tip and the neural tissue can cause hysteresis in the recorded neural signal. It is believed that there may be stiction between the electrode tip and the tissue. Additionally, because of tissue compression from the electrode insertion, when the electrode moves backward, the tissue may relax with it, resulting in a smaller relative movement between the electrode tip and the tissue than expected. This hysteresis is highly variable in magnitude, limits control action, and adds uncertainty to the electrode placement.
Finally, neuron damage can be a challenge. The electrode can potentially puncture and damage neurons when the electrode moves to achieve isolation or the neural tissue relaxes towards a stationary electrode.
Creating mechanisms and devices for small bio-robotic devices, such as chronic multi-electrode implants and microdrives, poses design and manufacturing challenges that strain the capabilities of traditional manufacturing processes even at the mesoscale level. Traditional manufacturing techniques rely on assemblies of pre-manufactured parts and fasteners that can compromise the reliability of the device. Fasteners and connectors not only take up a large percentage of the design volume at small scales, but can often work themselves loose or give way to leaks in the wet conditions of living tissue. Devices constructed using conventional fastening techniques may also lack the durability required to withstand frequent sterilization required for their use.
A chronic or semi-chronic microdrive must have an overall size and weight rendering it suitable for implantation in subjects without significantly affecting their awake behavior. Microdrives include actuators that position the electrodes coupled to the microdrive. Generally, each electrode is attached to a separate actuator that positions only the electrode attached thereto. Many commercially available prior art microdrives include relatively large actuators designed to position the electrodes during acute recording. Generally speaking, commercially available microdrives (e.g., those available from Thomas Recording GmbH, Germany; FHC Inc., USA; and Narishige Inc., Japan) are too large to be practical for chronic use. Other examples of microdrives that are too large for chronic or semi-chronic recording applications include commercially available microdrives (e.g., the LSS-8000 system produced by GMP, Lausanne, Switzerland) that use very large piezoelectric actuators to position the electrode(s). The size of these piezoelectric actuators limit the use of the such microdrives to acute recording experiments.
Previous work has described mesoscale microdrives designed for autonomous semi-chronic operation, while more recent work aims to apply MEMS technology to create arrays of multi-site electrodes motorized by hydrolysis-based actuators. See, e.g., J. G. Cham et al., “A Semi-Chronic Motorized Microdrive and Control Algorithm for Autonomously Isolating and Maintaining Optimal Extracellular Action Potentials,” J. Neurophysiol., 93:570-79 (January 2005); and R. A. Andersen et al., “Cognitive Neural Prosthetics,” Trends in Cog. Sci, 8(11):486-493 (November 2004).
Miniature actuators often have very small force output, and require special attention to minimize losses in power from, for example, friction due to misalignment. High precision movement is necessary to obtain optimal signal quality, given that action potentials from a typical neuron may be lost by movements as small as a few microns. Gears and lead screws, which are commonly used, often introduce a significant amount of imprecision in the microdrive due to gearing backlash. A relatively long stroke is also needed, because a range of motion of several millimeters, if not centimeters, is often required depending on the depth of the target brain structure, and the accuracy of the implantation procedure. The microdrive must also be able to keep the electrodes stable while subjected to significant stresses and vibrations from the freely moving subject. Further, the size requirement may limit the number of actuators that can be packaged in the microdrive, and the compactness and proximity of all the electrical pathways may increase noise and interference in the neural signal recorded.
Non-traditional manufacturing techniques, such as layered manufacturing, in which parts and mechanisms are “grown” in layers, allow intricate structures to be made with nearly arbitrary geometry and few seams. See, e.g., J. G. Cham et al., “Layered Manufacturing with Embedded Components: Process Planning Issues,” ASME Proc., DETC '99, Las Vegas, Nev., (September, 1999); and J. G. Cham et al., “Fast and Robust: Hexapedal Robots via Shape Deposition Manufacturing,” Intl. J. Robotics Res., 21(10-11):869-882 (2002). Many of these processes, however, are limited by the bio-incompatibility of the materials available through these processes.
Therefore a need in the art exists for neural prosthetic devices, and in particular, microdrives, suitable for chronic, implantable use. There is a further need in the art for computational technology that can isolate a neural signal originating from a single neuron within a recording containing one or more neural signals. A further need exists for computational technology that can maintain a suitable signal quality of the neural signal isolated in the recordings. A need also exists for computational technologies that may be used in connection with neural interface microdrives capable of positioning electrodes to record signals from active neurons. Similarly, there is a need for computational technologies to actively and autonomously position electrodes during acute recordings. Such technologies may increase the efficiency and/or quality of scientific research and experiments. A need also exists for miniature semi-chronic micro-drives capable of tracking the plasticity (or adaptability) of individual neurons over days and weeks.